What are the main challenges of memory in machine learning?

What are the main challenges of memory in machine learning?

The main challenges include limited memory capacity, data retention, and the trade-off between speed and accuracy.

How does limited memory affect model performance?

Limited memory can lead to overfitting, as models may not generalize well to new data if they can't store enough information.

What strategies can be used to overcome these memory challenges?

Strategies include using memory-efficient algorithms, data compression techniques, and leveraging external memory systems.

Can you give an example of a memory-efficient algorithm?

Sure! One example is the k-means clustering algorithm, which can be optimized to use less memory by processing data in batches.